1. Field of the Invention
The present invention generally relates to a method of filtering noise of a source digital data which consists of signal and noise, and more particularly, to a method of filtering a source digital audio data with short delay, which can be applied to a signal with a correlation characteristic such as audio stream to be broadcasted or recorded using a predetermined media.
2. Description of the Related Art
Generally, noise reduction is a kind of issues proposed in various applications. Many solutions to reduce noise have been suggested and applied. Unfortunately, there is no excellent solution since the noise reduction efficiency depends on signal source, noise characteristics and environment and the noise reduction requires complex calculation and process delay. The object of noise reduction for audio signal is to lower noise level without any distortion of signal.
Main applications of noise reduction for audio signals are local and long-distance telecommunication, answering machine and wireless telephone, hands-free speakerphone, mobile telephone, airplane audio communication machine, voice recognition unit, etc.
The most popular and effective methods of reducing noise are spectral subtraction, various approaches related to optimal filtering such as Wiener filtering, Ephraim and Malah weighting law and approaches based on psychoacoustic model. The documents in which these methods are disclosed in detail are as follows: A. Akbari Azirani, R. Le Bouquin Jeannes and G. Faucon, “Speech enhancement using a Wiener filtering under signal presence uncertainty”, proceedings Europe signal processing conference, Trieste, Italy, September, 1996; S. F. Boll, “Suppression of acoustic noise in speech using spectral subtraction”, IEEE transaction on acoustics, speech and signal processing, vol. 27, no. 2, April, 1979; Y. Ephraim and D. Malah, “Speech enhancement using minimum mean-square error short-time spectral amplitude estimator”, IEEE transaction on acoustics, speech and signal processing, vol. 32, no. 6, December, 1994; Y. Ephraim and D. Malah, “Speech enhancement using minimum mean-square error log-spectral amplitude estimator”, IEEE transaction on acoustics, speech and signal processing, vol. 33, no. 2, April, 1985; S. Gustafsson, P. Jax and P. Vary, “A novel psychoacoustically motivated audio enhancement algorithm preserving background noise characteristics”, proceedings international conference on acoustics, speech and signal processing,. Seattle, USA, May, 1998; S. Gustafsson, P. Jax and P. Vary, “A new approach to noise reduction based on auditory masking effects”, ITG-Fachbericht 152: Sprachkommunication, Dresden, Germany, August/September, 1998; ISO/IEC, “International standard 11172-3:1993, information technology-coding of moving pictures and associated audio for digital storage media at up to about 1.5 mbit/s-part 3, audio”, 1993; P. Vary, “Noise suppression by spectral magnitude estimation-mechanism and theoretical limits”, vol. 8, no. 4, 1985; S. V. Vaseghi, “Advanced signal processing and digital noise reduction”, John Wiley and Teubner, 1996; N. Virag, “Speech enhancement based on masking properties of the auditory system”, proceedings international conference on acoustics, speech and signal processing, Detroit, USA, May, 1995; and E. Zwicker and H. Fastl, “Psychoacoustics; Facts and Models”, Springer-Verlag, New York, 1990.
The various above-mentioned approaches have their own advantages and problems but the most popular and effective noise-concealment algorithm is block oriented and requires essential delay (20 ms or longer). So, these algorithms are not suitable for some applications with short delay, for example, an application based on ITU G.726 standard (1990, ITU recommendation G.726 adaptive differential pulse code modulation (ADPCM) of 40, 32, 24 and 16 Kbps).
These G.726 standard application examples are video conference system, multimedia, flight record, ISDN and satellite communication network, wireless digital telephone communication, radio/wireless local loop, pair-gain, etc.